



'''simple docstring'''






import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase:   List[Any]					=		'.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowerCAmelCase:   Tuple					=		[
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]
def  lowerCamelCase__			(		_A					,     _A					,     _A							):
  a		:   Tuple	  =	SavedModel()
  a		:   List[Any]	  =	[]

  with open(os.path.join(_A					,     'utils'					,     'tf_ops'					,     'onnx.json'							)							) as f:
    a		:   Optional[int]	  =	json.load(_A							)['opsets']

  for i in range(1					,     opset + 1							):
    onnx_ops.extend(onnx_opsets[str(_A							)]							)

  with open(_A					,     'rb'							) as f:
    saved_model.ParseFromString(f.read()							)

  a		:   str	  =	set()

  # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
  for meta_graph in saved_model.meta_graphs:
    # Add operations in the graph definition
    model_op_names.update(node.op for node in meta_graph.graph_def.node							)

    # Go through the functions in the graph definition
    for func in meta_graph.graph_def.library.function:
      # Add operations in each function
      model_op_names.update(node.op for node in func.node_def							)

    # Convert to list, sorted if you want
  a		:   Optional[Any]	  =	sorted(_A							)
  a		:   List[str]	  =	[]

  for op in model_op_names:
    if op not in onnx_ops and op not in INTERNAL_OPS:
      incompatible_ops.append(_A							)

  if strict and len(_A							) > 0:
    raise Exception(f"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops							)
  elif len(_A							) > 0:
    print(f"""Found the following incompatible ops for the opset {opset}:"""							)
    print(*_A					,     sep='\n'							)
  else:
    print(f"""The saved model {saved_model_path} can properly be converted with ONNX."""							)


if __name__ == "__main__":
     lowerCAmelCase:   int					=		argparse.ArgumentParser()
     parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
     parser.add_argument(
         '--opset', default=1_2, type=int, help='The ONNX opset against which the model has to be tested.'
     )
     parser.add_argument(
         '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
     )
     parser.add_argument(
         '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
     )
     lowerCAmelCase:   Union[str, Any]					=		parser.parse_args()

     if args.framework == "onnx":
          onnx_compliancy(args.saved_model_path, args.strict, args.opset)




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:   List[Any]					=		{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Dict					=		['ReformerTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Tuple					=		['ReformerTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ReformerAttention',
         'ReformerForMaskedLM',
         'ReformerForQuestionAnswering',
         'ReformerForSequenceClassification',
         'ReformerLayer',
         'ReformerModel',
         'ReformerModelWithLMHead',
         'ReformerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer import ReformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer_fast import ReformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_reformer import (
              REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              ReformerAttention,
              ReformerForMaskedLM,
              ReformerForQuestionAnswering,
              ReformerForSequenceClassification,
              ReformerLayer,
              ReformerModel,
              ReformerModelWithLMHead,
              ReformerPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase:   str					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)